# The Role of GitHub Copilot on Software Development: A Perspective on Productivity, Security, Best Practices and Future Directions

arXiv: https://arxiv.org/abs/2502.13199 · HTML: https://mlsanity.com/paper/2502.13199/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 13 of 13)

- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [GitHub Copilot AI pair programmer: Asset or Liability?](https://mlsanity.com/paper/2206.15331/) (2022)
- [Productivity Assessment of Neural Code Completion](https://mlsanity.com/paper/2205.06537/) (2022)
- [Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT](https://mlsanity.com/paper/2304.10778/) (2023)
- [On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilot](https://mlsanity.com/paper/2302.00438/) (2023)
- [Assessing the Security of GitHub Copilot Generated Code -- A Targeted Replication Study](https://mlsanity.com/paper/2311.11177/) (2023)
- [When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming](https://mlsanity.com/paper/2306.04930/) (2023)
- [Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming](https://mlsanity.com/paper/2111.07875/) (2021)
- [Experience with GitHub Copilot for Developer Productivity at Zoominfo](https://mlsanity.com/paper/2501.13282/) (2025)
- [Comprehensive Evaluation and Insights into the Use of Large Language Models in the Automation of Behavior-Driven Development Acceptance Test Formulation](https://mlsanity.com/paper/2403.14965/) (2024)
- [GitHub Copilot: the perfect Code compLeeter?](https://mlsanity.com/paper/2406.11326/) (2024)
- [The Impact of AI Tool on Engineering at ANZ Bank An Empirical Study on GitHub Copilot within Corporate Environment](https://mlsanity.com/paper/2402.05636/) (2024)
- [UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices](https://mlsanity.com/paper/2501.14172/) (2025)

## Built on by (most cited first, 1 of 1)

- [From Overload to Insights: How AI Agents Can Support Scientists in Analyzing Complex Data](https://mlsanity.com/paper/2607.16845/) (2026)

## Abstract (arXiv)

GitHub Copilot is transforming software development by automating tasks and boosting productivity through AI driven code generation. In this paper, we conduct a literature survey to synthesize insights on Copilot's impact on productivity and security. We review academic journal databases, industry reports, and official documentation to highlight key findings and challenges. While Copilot accelerates coding and prototyping, concerns over security vulnerabilities and intellectual property risks persist. Drawing from the literature, we provide a perspective on best practices and future directions for responsible AI adoption in software engineering, offering actionable insights for developers and organizations to integrate Copilot effectively while maintaining high standards of quality and security.